Bibliographic record
Abstract
Changes 🐛 Bug Fixes [FIX] change error message when no input found @Remi-Gau (#231) Block components with same names in dataset init @pvandyken (#228) Use entity-specific parsers @pvandyken (#229) Check if BidsDir exists before indexing @pvandyken (#218) 🧰 Maintenance Skip Bids Indexing when custom_paths specified @pvandyken (#224) Split Dataset and Component models into new file @pvandyken (#225) Update setup-poetry to v4 @pvandyken (#214) Transition versioning to dynamic-poetry-versioning @pvandyken (#210) Remove redundant inputs from github actions @pvandyken (#211) Update auto-assign action version @pvandyken (#212) 📝 Documentation Update some outdated references to BidsInputs @pvandyken (#226) Fix docstring type in _get_lists_from_bids @pvandyken (#217)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.288 | 0.326 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".